Catálogo de publicaciones - libros

Science has been researching on the physiology of the human hearing, and in the last decades, on the mechanism of the neural stimulus generation towards the nervous system. The objective of this research is to develop an algorithm that generalizes the stochastic spike pattern of the auditory nerve fibers (ANF) formulated by Meddis, which fulfils the Volley principle (principle that better describes the operation of the auditory system). The operating principle of the peripheral auditory system together with the models chosen to stimulate the auditory system and the characteristics of the implemented computational model are herein described. The implementation and analysis of the stochastic spike of a simple ANF and the spatial and spatial–temporal stochastic stimulation models demonstrate the superiority of the latter.

The issue of classification has long been a central topic in the analysis of multielectrode data, either for spike sorting or for getting insight into interactions among ensembles of neurons. Related to coding, many multivariate statistical techniques such as linear discriminant analysis (LDA) or artificial neural networks (ANN) have been used for dealing with the classification problem providing very similar performances. This is, there is no method that stands out from others and the right decision about which one to use is mainly depending on the particular cases demands. In this paper, we found groups of rabbit ganglion cells with distinguishable coding performances by means of a simple based on behaviour method. The method consisted of creating population subsets based on the autocorrelograms of the cells and grouping them according to a minimal Euclidian distance. These subpopulations shared functional properties and may be used for functional identification of the subgroups. Information theory (IT) has been used to quantify the coding capability of every subpopulation. It has been described that all cells that belonged to a certain subpopulation showed very small variances in the information they conveyed while these values were significantly different across subpopulations, suggesting that the functional separation worked around the capacity of each cell to code different stimuli. In addition, the overall informational ability of each of the generated subpopulations kept similar. This trend was present for an increasing number of classes until a critical value was reached, proposing a natural value for functional classes.

This article presents a neural network model for the simulation of the neurological mechanism that produces limbs hiper-rigidity (spasticity). In this model, we take into account intrinsic plasticity, which is the property of biological neurons that consists in the shifting of the action potential threshold according to experience. In accordance to the computational model, a therapeutic technique for diminishing limbs spasticity is proposed and discussed.

A comprehensive view of speech and voice technologies is now demanding better and more complex tools amenable of extracting as much knowledge about sound and speech as possible. Many knowledge-extraction tasks from speech and voice share well-known procedures at the algorithmic level under the point of view of bio-inspiration. The same resources employed to decode speech phones may be used in the characterization of the speaker (gender, age, speaking group, etc.). Based on these facts the present paper examines a hierarchy of sound processing levels at the auditory and perceptual levels on the brain neural paths which can be translated into a bio-inspired audio-processing architecture. Through this paper its fundamental characteristics are analyzed in relation with current tendencies in cognitive audio processing. Examples extracted from speech processing applications in the domain of acoustic-phonetics are presented. These may find applicability in speaker’s characterization, forensics, and biometry, among others.

An architecture for a cortical stimulator with visual neuroprosthetic purposes is presented. This device uses a 3D penetrating multielectrode array, which will be implanted in V1, offering different signal amplitude sets with the programable current source module. This electrode array has been proved for injecting current (charge) in a safety, secure and precise way during animal acute experimentation. The dynamic characteristic of the stimulator provide the possibility to adapt the current level to the different electrodes and tissue impedances. The architectureis based on a microprocessor circuit with programmable waveforms with a transistor based current injection stage. With the proposed system, a wide stimuli set can be used for obtaining the optimal parameters to use in a visual neuroprosthesis using as input a retinomorphic system. The histological results validate the stimulation and implantation procedures.

This is a brief survey of spiking neural P systems, a branch of membrane computing recently introduced with motivation from neural computing. Basic ideas, examples, some results, and several research topics are presented.

Tissue P systems with cell division is a computing model in the framework of Membrane Computing based on intercellular communication and cooperation between neurons. The ability of cell division allows us to obtain an exponential amount of cells in linear time and to design cellular solutions to -complete problems in polynomial time. In this paper we present a solution to the Subset Sum problem via a family of such devices. This is the first solution to a numerical -complete problem by using tissue P systems with cell division.

We study a Păun’s conjecture concerning the unsolvability of –complete problems by polarizationless P systems with active membranes in the usual framework, without cooperation, without priorities, without changing labels, using evolution, communication, dissolution and division rules, and working in maximal parallel manner. We also analyse a version of this conjecture where we consider polarizationless P systems working in the minimally parallel manner.

Microfluidic systems, which constitute a miniaturization of a conventional laboratory to the dimensions of a chip, are expected to become the key support for a revolution in the world of biology and chemistry. This article proposes a parallel algorithm that uses DNA and such a distributed microfluidic device to generate scheduling grids in polynomial time. Rather than taking a brute force approach, the algorithm presented here uses concatenation and separation operations to gradually build the DNA strings that represent a Multiprocessor Task scheduling problem grids. The microfluidic device used makes for an autonomous system, also enabling it to solve the problem without the need of external control.

In certain classes of chemical reaction networks (CRN), there may be two stable states. The challenge is to find a model of the CRN such that the stability properties can be predicted. In this paper we consider the problem of building a P-system designed to simulate the CRN in an attempt to determine if the CRN is stable or bistable. We found that for the networks in [2] none of the bistable CRN would have a bistable P-system by stoichiometry alone. The reaction kinetics must be included in the P-system model; the implementation of which has been considered an open problem. In this paper we conclude that a P-system for a CRN in reactants and products has at most 2( + ) membranes and 6( + ) rules. This suggests that P-system models of a chemical reaction network, including both stoichiometry and reaction kinetics can be built.